Big data is on the rise, and IT professionals in financial services firms are turning to Apache Hadoop to improve operational efficiencies, reduce risk, analyze fraud patterns, and reduce customer churn. Let’s take a look at some key indicators that demonstrate that big data has indeed arrived at your firm’s doorstep.
1. You’re Overwhelmed by the Amount and Types of Customer Data
Are you using multiple sources to pull in your data? Are you finding that there’s too much data to handle? Without big data technologies, it becomes difficult to get different silos of data to work together. Big data technologies, such as Hadoop, can act as an enterprise data hub (EDH) at the heart of your infrastructure, giving you the ability to store, transform and analyze your data in one place. Let’s look at a real world example.
Let’s say that your firm’s marketing department wants to improve their customer service. By using Hadoop to analyze unstructured data (social media profiles, calls, complaint logs, discussion forms, emails, etc.), you can gain a much deeper understanding of your customers’ needs, making it easier to provide them with the appropriate financial products and services.
2. You Need a Better Way to Gather and Process Your Risk Data
You can use Hadoop to efficiently gather and process all of your risk data so that you can focus on measuring financial performance against risk tolerance, satisfy reporting requirements, determine a consumer’s likelihood of paying back debt, and analyze financial reports. Historical data is responsible for a large amount of data insights. With Hadoop, you can perform a historical analysis of risk data on demand, and get real-time alerts when limits are breached.
3. You're Struggling to Find the Right Tools for Assessing Credit Risk
The rules for determining whether or not to give a customer a loan are much more stringent these days, so you need a more accurate method for determining a person’s credit risk. By using Hadoop, you can pull in customer data on everything from deposit information to credit card purchase history in order to get a complete, holistic view of your customers.
4. You Need a Better Counterparty Risk Analytics System
As you know, calculating counterparty risk requires much more than computing a formula. If you need to run long and complex “Monte Carlo simulations” in order to get a complete picture of risk exposure, you’ll need a tool like Hadoop in order to provide the performance, scalability, reliability, and easy data access required as part of a reliable, secure, counterparty risk analytics system.
5. Your Data Storage Is “Breaking the Bank”
If your data storage expansion is becoming too costly, you should be looking at a more scalable big data alternative such as Hadoop. Since Hadoop runs off on commodity hardware, scaling up your data center isn’t an expensive proposition.
6. Your Data Is Slowing You Down
When your data becomes more than then your tools can manage, it can create a noticeable lag. In addition, conventional data management tools can slow down your performance. With Hadoop, you can access big data sets that previously were too large to hold in memory or took too long to load, which means you can avoid costly data loss and downtime.
There are many IT departments in financial institutions that don’t realize that they’ve crossed the threshold to where big data toolsets would be a profitable investment. By reviewing these six indicators, you can better determine whether big data is right for your firm.
By Sameer Nori, Senior Product Marketing Manager, MapR Technologies